Understanding the consequences of changes in social networks is an important an-

thropological research goal. This dissertation looks at the role of data-driven social

networks on infectious disease transmission and evolution. The dissertation has two

projects. The first project is an examination of the effects of the superspreading

phenomenon, wherein a relatively few individuals are responsible for a dispropor-

tionate number of secondary cases, on the patterns of an infectious disease. The

second project examines the timing of the initial introduction of tuberculosis (TB) to

the human population. The results suggest that TB has a long evolutionary history

with hunter-gatherers. Both of these projects demonstrate the consequences of social

networks for infectious disease transmission and evolution.

The introductory chapter provides a review of social network-based studies in an-

thropology and epidemiology. Particular emphasis is paid to the concept and models

of superspreading and why to consider it, as this is central to the discussion in chapter

2. The introductory chapter also reviews relevant epidemic mathematical modeling

studies.

In chapter 2, social networks are connected with superspreading events, followed

by an investigation of how social networks can provide greater understanding of in-

fectious disease transmission through mathematical models. Using the example of

SARS, the research shows how heterogeneity in transmission rate impacts super-

spreading which, in turn, can change epidemiological inference on model parameters

for an epidemic.

Chapter 3 uses a different mathematical model to investigate the evolution of TB

in hunter-gatherers. The underlying question is the timing of the introduction of TB

to the human population. Chapter 3 finds that TB’s long latent period is consistent

with the evolutionary pressure which would be exerted by transmission on a hunter-

igatherer social network. Evidence of a long coevolution with humans indicates an

early introduction of TB to the human population.

Both of the projects in this dissertation are demonstrations of the impact of var-

ious characteristics and types of social networks on infectious disease transmission

dynamics. The projects together force epidemiologists to think about networks and

their context in nontraditional ways.